GLÀFF, a Large Versatile French Lexicon
Nabil Hathout, Franck Sajous, Basilio Calderone
CLLE-ERSS, CNRS & Université de Toulouse
{nabil.hathout, franck.sajous, basilio.calderone}@univ-tlse2.fr
Abstract
This paper introduces GLÀFF, a large-scale versatile French lexicon extracted from Wiktionary, the collaborative online dictionary.
GLÀFF contains, for each entry, inflectional features and phonemic transcriptions. It distinguishes itself from the other available French
lexicons by its size, its potential for constant updating and its copylefted license. We explain how we have built GLÀFF and compare it
to other known resources in terms of coverage and quality of the phonemic transcriptions. We show that its size and quality are strong
assets that could allow GLÀFF to become a reference lexicon for French NLP and linguistics. Moreover, other derived lexicons can
easily be based on GLÀFF to satisfy specific needs of various fields such as psycholinguistics.
Keywords: Inflectional and phonological lexicon, free lexical resources, French Wiktionary
1.
Introduction
This article introduces GLÀFF,1 a large versatile French
lexicon extracted from Wiktionnaire, the French edition of
Wiktionary. Wiktionnaire contains more than 2 million articles, each including definitions, pronunciations, translations and semantic relations. GLÀFF aims to make this
resource available for NLP systems and linguistic research
in a workable format.
Some French morphological lexicons, such as
Lefff (Clément et al., 2004) and Morphalou (Romary
et al., 2004), are freely available. These resources contain
inflected forms, lemmas and morphosyntactic tags. They
do not include, however, phonemic transcriptions that are
necessary in phonology and in the design of tools such as
phonetizers. Lexique (New, 2006), another free lexicon,
contains phonemic transcriptions but has a restricted coverage. While this lexicon is popular in psycholinguistics,
its sparsity in terms of inflected forms prevents its use in
NLP. Resources that have both exploitable coverage and
phonemic transcriptions, such as BDLex (Pérennou and de
Calmès, 1987), ILPho (Boula De Mareuil et al., 2000) or
GlobalPhone (Schultz et al., 2013) are not free. Besides
the cost, derivative works cannot be redistributed, which
constitutes an impediment for collaborative research.
As of today, no French lexicon meets all following requirements: free license, wide coverage, and phonemic transcriptions. Wiktionnaire may be a candidate resource for
the creation of such a lexicon. Wiktionary was first used
for NLP by Zesch et al. (2008) to compute semantic relatedness. Its potential as an electronic lexicon was first studied for English and French by Navarro et al. (2009). Other
works tackled data extraction from other language editions.
Anton Pérez et al. (2011) describe the integration of the
Portuguese Wiktionary and Onto.PT (Gonçalo Oliveira and
Gomes, 2010). Sérasset (2012) built Dbnary, a multilingual network containing “easily extractable” entries. For
French, the resulting graph includes 260,467 nodes. OntoWiktionary (Meyer and Gurevych, 2012), an ontology
based on Wiktionary, and UBY (Gurevych et al., 2012), an
alignment of 7 resources including WordNet, Germanet and
1
GLÀFF is freely available at http://redac.
univ-tlse2.fr/lexicons/glaff_en.html
Wiktionary, constitute the most complete resources based
on Wiktionary. A detailed characterization of the English
and French editions of Wiktionary is given in (Sajous et
al., 2010; Sajous et al., 2013b). These papers also present
the extraction process of WiktionaryX,2 an XML-structured
lexicon containing definitions, semantic relations and translations. GLÀFF is a new step focusing on the extraction of
inflected forms and phonemic transcriptions that were absent from the previous resource.
Wiktionary’s language editions are released as “XML
dumps”, where only the macrostructure is marked by XML
tags. The microstructure is encoded in a format called wikicode, whose syntax is not formally defined, evolves over
time, and is not stable from one language edition to another. Due to this underspecified syntax, a parser has to
expect multiple deviations from the “prototypical article”
and must handle missing information, redundancy and inconsistency. For example, the gender or pronunciation may
be missing in an inflected form’s article, but occur in the
one dedicated to its lemma. Sometimes, contradictory information may occur in both articles. To build GLÀFF, we
designed an extractor that collects the maximum amount of
information from Wiktionary’s articles (lemmas, inflected
forms and conjugation tables) and applies a set of rules to
output a structured and (as much as possible) consistent inflectional and phonological lexicon.
2.
Resource description
GLÀFF contains more than 1.4 million entries including
nouns, verbs, adjectives, adverbs and function words. As
illustrated in Figure 1, each entry contains a wordform, a
tag in GRACE format (Rajman et al., 1997), a lemma and
an IPA transcription, when present in Wiktionnaire. Entries also contain word frequencies computed over different corpora. Sajous et al. (2013a) give a first description of GLÀFF. We characterize GLÀFF below in terms of
coverage (section 2.1.) and phonemic transcriptions (section 2.2.). In section 2.3., we present newly added features.
2
WiktionaryX is freely available at: http://redac.
univ-tlse2.fr/lexicons/wiktionaryx_en.html
1007
affluent|Afpms|affluent|a.fly.ã|12|0.41|15|0.51|175|0.79|183|0.83|576|0.45|696|0.55
affluente|Afpfs|affluent|a.fly.ãt|0|0|0|0|2|0.00|183|0.83|9|0.00|696|0.55
affluentes|Afpfp|affluent|a.fly.ãt|1|0.03|15|0.51|1|0.00|183|0.83|22|0.01|696|0.55
affluent|Ncms|affluent|a.fly.ã|22|0.76|38|1.31|232|1.05|444|2.02|1234|0.98|3655|2.91
affluents|Afpmp|affluent|a.fly.ã|2|0.06|15|0.51|5|0.02|183|0.83|89|0.07|696|0.55
affluents|Ncmp|affluent|a.fly.ã|16|0.55|38|1.31|212|0.96|444|2.02|2421|1.93|3655|2.91
affluent|Vmip3p-|affluer|a.fly|9|0.31|187|6.48|369|1.67|1207|5.49|500|0.39|1929|1.53
affluent|Vmsp3p-|affluer|a.fly|9|0.31|187|6.48|369|1.67|1207|5.49|500|0.39|1929|1.53
Figure 1: Extract of GLÀFF
Lexique
BDLex
Lefff
Morphalou
GLÀFF
Categorized inflected forms
Simples Non simples
Total
147,912
4,696
152,608
431,992
4,360
436,352
466,668
3,829
470,497
524,179
49
524,228
1,401,578
24,270 1,425,848
Categorized lemmas
Simples Non simples
Total
46,649
3,770
50,419
47,314
1,792
49,106
54,214
2,303
56,517
65,170
7
65,177
172,616
13,466 186,082
Table 1: Size of the lexicons (restricted to nouns, verbs, adjectives and adverbs).
2.1.
Coverage
GLÀFF differs from the lexicons currently used in NLP and
psycholinguistics by its exceptional size. Table 1 shows
the number of lemmas and inflected forms, simple (letters
only) and non-simple (containing spaces, dashes or digits).
GLÀFF contains 3 to 4 times more tokens and 3 to 9 times
more forms. This size is an important asset when the lexicon is used for research in derivational or inflectional morphology. It is also an advantage for the development of
NLP tools as morphosyntactic taggers and parsers. The table also shows that GLÀFF contains numerous multi-word
expressions (MWE) that can improve text segmentation and
subsequent processing.
The following comparisons only concern nouns, verbs, adjectives and adverbs. They were carried out on simple inflected forms and lemmas in order to ignore differences in
the treatment of MWEs and corpora segmentation. MWEs
(i.e. the 24 270 non simple forms –resp. 13 466 non simple
lemmas–) have been discarded from the version of GLÀFF
presented in this paper and will be added in a future version.
We first study the intersection of GLÀFF and other lexicons. We observe in Table 2 that the size of the intersections
directly depends on that of the lexicons: the bigger a lexicon, the larger its intersection with the other ones. The five
lexicons fall into three groups. Lexique has a smaller coverage. It only contains 9% of GLÀFF entries and 22% to 26%
of the entries of other lexicons. BDLex, Lefff and Morphalou cover 76% to 80% of Lexique and 30% of GLÀFF
in average. GLÀFF is clearly above with a coverage of 85%
to 93%. Its coverage is 5% to 65% larger than the ones of
the other lexicons.
GLÀFF is considerably larger than all other lexicons,
which potentially is an asset. In order to check that this
advantage is real (i.e. that having a greater number of lexemes and inflected forms is actually useful), we compared
the five lexicons to the vocabulary of three corpora of various types. LM10 is a 200 million word corpus made up of
the archives of the newspaper Le Monde from 1991 to 2000.
The second corpus, containing 260 million word, consists
of articles from the French Wikipedia. Finally, FrWaC (Baroni et al., 2009) is a 1.6 billion word corpus of French web
pages (spidered from the .fr domain).
Table 3 shows the coverage of the five lexicons with respect to the three corpora. The vocabulary is restricted to
the forms of frequency greater than or equal to 1, 2, 5, 10,
100 and 1000. The ranking of the corpora by coverage
is the same for the five lexicons. Although their size affects the order, their nature is also crucial. For example,
FrWaC being a collection of web pages, it contains a large
number of “noisy” forms (foreign words, missing or extra
spaces, missing diacritics, random spelling, etc.). Again,
we see the division of lexicons into three groups. BDLex,
Lefff and Morphalou have a quite close coverage. Lexique
has the smallest coverage up to the 100 threshold. GLÀFF
has the largest coverage for all corpora, except for LM10 at
the 1000 threshold where it is surpassed by Lefff by 0.2%.
For the other corpora and up to the 100 threshold, the size
of GLÀFF explains its larger coverage with respect to the
other lexicons (at the threshold 1, 14% to 53% larger for
LM10 and 30% to 120% larger for FrWaC; at the threshold 10, 4% to 16% for LM10 and 15% to 47% for FrWaC).
NLP tools that integrate GLÀFF should therefore offer an
improved performance in the treatment of these corpora.
Figure 2 compares the lexicons’ coverage from another perspective: for each lexicon, it represents the number of forms
having a corpus frequency within a given interval. We still
Lexique
Lexique
BDLex
Lefff
Morph.
GLÀFF
76.0
79.5
79.6
84.8
BDLex
26.0
86.3
85.4
93.3
Lefff
25.2
79.9
81.2
90.2
Morph.
22.5
70.4
72.3
GLÀFF
8.9
28.8
30.1
32.0
85.7
Table 2: Coverage w.r.t. the other lexicons (% of categorized inflected forms).
1008
Threshold: frequency ≥
# forms
Lexique
BDLex
LM10
Lefff
Morphalou
GLÀFF
# forms
Lexique
BDLex
Wikipédia
Lefff
Morphalou
GLÀFF
# forms
Lexique
BDLex
FrWaC
Lefff
Morphalou
GLÀFF
1
300,606
29.59
37.77
39.64
39.06
45.24
953,920
9.13
12.29
12.88
13.05
16.42
1,624,620
5.83
9.36
9.85
10.09
13.13
2
172,036
47.28
55.79
58.22
56.82
63.83
435,031
18.27
22.89
23.94
23.96
29.00
846,019
10.85
15.85
16.67
16.89
21.13
5
106,470
65.23
71.76
74.33
71.92
78.63
216,210
31.52
36.80
38.26
37.87
44.13
410,382
20.84
27.28
28.57
28.53
34.29
10
77,936
76.31
80.93
83.20
80.32
86.23
136,531
43.03
48.04
49.65
48.87
55.45
255,718
30.81
37.48
39.16
38.68
45.35
100
29,388
93.81
95.53
95.99
93.27
96.46
35,621
78.58
79.39
80.57
78.74
83.21
74,745
66.00
69.61
71.61
69.36
76.39
1000
7,838
98.58
98.69
98.90
97.48
98.68
7,956
95.72
95.33
95.71
94.16
96.10
22,100
89.47
90.03
91.16
88.51
92.76
Table 3: Lexicon/corpus coverage (% of non-categorized inflected forms).
observe the distribution of the lexicons into 3 groups. The
diagram also shows that even for very frequent and well established words, with a frequency between 101 and 1000,
GLÀFF’s coverage remains the largest. Table 3 and Figure 2 show that the superiority of GLÀFF is stronger for
heterogeneous corpora and for low and medium frequency
words. We complete the characterization of GLÀFF’s coverage by focusing on its specific vocabulary, i.e. on the
forms that are missing in the other four lexicons. Table 4
shows the number of forms that occur in the corpus for each
sub-vocabulary. In accordance with intuition, the number
of inflected forms increases with corpus size. The size of
the corpus, however, does not explain all. A large portion of the specific vocabulary consists of inflected verb
forms, because GLÀFF includes all their possible inflec-
10
x 10
tions. GLÀFF also contains less normative and more recent
French words which tend to appear in heterogeneous corpora such as FrWaC. Even for a newspaper corpus whose
most recent year is 2000 (LM10), Wiktionary’s “youth”
and constant updating allow GLÀFF to cover a number
of quite usual words such as: attractivité ‘attractivity’,
brevetabilité ‘patentability’, diabolisation ‘demonization’,
employabilité ‘employability’, homophobie ‘homophobia’,
hébergeur ‘host’, fatwa, institutionnellement ‘institutionally’, anticorruption ‘anti-corruption’, etc. missing from
the other lexicons.
Lexique
BDLex
Lefff
Morphalou
GLÀFF
4
GLÀFF
Morphalou
Lefff
BDLex
Lexique
9
8
Specific
forms
1 509
3 981
11 050
26 881
665 290
Number of attested forms
LM10 Wikipédia FrWaC
863
1 073
1 320
521
1 004
1 496
1 479
2 214
3 288
1 912
3 995
6 425
13 525
29 230
47 549
Table 4: Attestation of the lexicons’ specific vocabulary in
the corpora.
Number of forms
7
2.2.
6
5
4
3
2
1
1-10
11-102
101-103
1001-104
10001-105 100001-106
Frequency intervals
Figure 2: Distribution of forms w.r.t. their corpus frequency.
Phonemic transcriptions
GLÀFF provides a phonemic transcription for about 90%
of the entries. We evaluated the consistency of these transcriptions with respect to those of BDLex and Lexique (after conversion into IPA encoding). Two types of comparisons were performed: a) phonological transcriptions; b)
syllabification (only for matching transcriptions). Tables
5a to 5c report the top ten variations between pairs from
the three lexicons. We only considered one phoneme differences, ignoring syllabification. Table 5d illustrates such
differences by reporting, for a small set of words, examples
of transcription adopted by the three lexicons and, in the
last column, additional transcriptions taken from the Dictionnaire de la Prononciation Française dans son Usage
1009
Oper.
r
r
r
r
r
r
r
r
r
d
Phonemes
E/e
O/o
o/O
y/4
@/ø
@/œ
u/w
b/p
s/z
j
%
48.18
32.17
11.02
1.83
1.44
1.39
0.84
0.73
0.51
0,25
P
%
48.18
80.36
91.37
93.21
94.64
96.03
96.87
97.61
98.12
98,37
Oper.
r
i
r
r
r
r
r
r
i
r
(a) BDLex/Lexique
Operation
r : E/e
r : s/z
r : b/p
r : o/O
r : @/ø/œ
r : y/4
r : u/w
r : a/A
i,d : i,j
i,d : @
Phonemes
O/o
@
e/E
E/e
A/a
s/z
@/ø
œ/ø
i
o/O
%
60.03
14.18
6.90
4.98
4.92
1.25
0.91
0.47
0.42
0.38
P
%
60.03
74.21
81.11
86.09
91.01
92.26
93.17
93.64
94.06
94.44
Oper.
r
r
i
r
r
r
r
r
i
r
(b) GLÀFF/Lexique
Form
été
stalinisme
obturer
pomme
heureux
gradué
jouer
inouı̈
pâte
riiez
contenu
BDLex
/E.te/
/sta.li.nis,m/
/Ob.ty.Ke/
/po,m/
/@.Kø/
/gKa.dy.e/
/Zu.e/
/i.nu.i/
/pa,t/
/Ki.i.je/
/kÕ,t@.ny/
Transcriptions
Lexique
GLÀFF
/e.te/
/e.te/
/sta.li.nizm/ /sta.li.nism/
/Op.ty.Ke/
/Op.ty.Ke/
/pOm/
/pOm/
/ø.Kø/
/œ.Kø/
/gKa.d4e/
/gKa.d4e/
/Zwe/
/Zwe/
/i.nwi/
/i.nwi/
/pat/
/pAt/
/Ki.je/
/Kij.je/
/kÕ.t@.ny/
/kÕt.ny/
Phonemes
e/E
O/o
@
o/O
A/a
4/y
œ/@
ø/@
i
w/u
%
66.46
10.58
5.90
4.36
3.84
1.61
1.09
0.86
0.84
0.79
P
%
66.46
77.05
82.96
87.32
91.17
92.78
93.88
94.74
95.58
96.38
(c) GLÀFF/BDLex
DPF
/ete/
/stalinism/, /stalinizm/
/Optyre/, /Obtyre/
/pOm/
/ørø, œrø/
/grad4e/, /grAd4e/, /gradye/
/Zwe/, /Zue/
/inwi/, /inui/
/pat/ , /pAt/
/kÕt(@)ny/
(d) Examples of inter-lexicons differences of phonemic transcription.
Table 5: The 10 most frequent differences in transcription.
Operations: r = replacement ; i = insertion ; d = deletion.
Lexicon
BDLex Lexique
GLÀFF Lexique
GLÀFF BDLex
Intersection
112,439
123,630
396,114
Phonological transcription
Identical
Comparable
58.31
96.88
79.50
97.81
61.72
96.88
Syllabification
Identical
98.92
98.48
98.30
Table 6: Inter-lexicon agreement: phonological transcriptions and syllabification
Réel (Martinet and Walter, 1973), or DPF. This dictionary
stems from a study of French pronunciation carried out in
1968-1973 involving 17 French speakers in order to test differences in production for individual words.
The differences in transcriptions between GLÀFF and the
other two lexicons are comparable to the differences observed between BDLex and Lexique. In particular, these
differences are mostly due to the distinctions between the
mid vowels, i.e. the front-mid vowels: [e] (close-mid) vs.
[E] (open-mid) and the back-mid vowels: [o] (close-mid)
vs. [O] (open-mid). This alternation is a well known aspect of French phonology resulting from diatopic variations
(North vs. South), as described in (Detey et al., 2010). Such
expected oppositions accounts for about 91% of the divergences between BDLex and Lexique.
Table 6 reports the percentage of identical phonological
transcriptions shared by the lexicons and the percentage
of the ‘comparable’ phonological transcriptions, i.e. disregarding the distinction between close-mid and open-mid
vowels. GLÀFF and Lexique give identical transcriptions for 79.5% of entries whereas the percentage between
GLÀFF and BDLex is lower, at 61.7%. Table 6 also reports
the results of the comparison of syllabification in the three
lexicons (performed on the basis of identical transcriptions
only). This comparison shows that the three lexicons are
quite similar with respect to syllabification (98%).
A crowdsourced resource like Wiktionary may reveal some
amateursims. However, crowdsourcing is interesting from
a linguistic point of view because it reflects the language
perception of speakers rather than of linguists. For example, word-medial consonant clusters like /s/ + C are treated
in GLÀFF sometimes as heterosyllabic clusters, as in ministère /mi.nis.tEK/ ‘ministry’, with the /s/ and the following consonant assigned to distinct syllables (corresponding
to the canonical analysis in French phonological tradition),
and sometimes as tautosyllabic clusters, as in monistique
/mO.ni.stik/ ‘monistic’. Such examples can reveal areas
of non-deterministic variation that standard lexicographic
1010
Figure 3: GLÀFFOLI, the GLÀFF OnLine Interface
conventions tend to minimize.
2.3.
Additional features
Version 1.2 of GLÀFF comes with form and lemma frequencies (absolute and relative) computed over different
corpora including LM10 and FrWaC (cf. Figure 1).
Another novelty is the possibility of browsing GLÀFF online thanks to the GLÀFFOLI interface,3 as illustrated in
Figure 3. This interface enables any user to build a multicriteria query. Request fields may include wordform,
lemma, part of speech and/or pronunciation written in IPA
or SAMPA. These fields are matched against GLÀFF entries through regular expressions or operators such as is,
contains, starts with, ends with, etc. depending on the
user’s choice. Display is customizable and, when corpora
frequencies are visible, the wordforms attested in FrWaC
are linked to the NoSkecthEngine (Rychlý, 2007) concordancer.
3.
Conclusion
We presented a new French lexicon built automatically
from Wiktionary. This lexicon is remarkable for its size.
It provides morphosyntactic descriptions for 1.4 million
entries and phonemic transcriptions for 1.3 million of them.
Despite its very large size, the overall quality of GLÀFF is
very good as shown by various comparisons with similar
resources including Lexique, Lefff and BDLex.
Among the directions for future research, we plan an
evaluation of the contribution of GLÀFF to syntactic
parsing using the Talismane parser (Urieli, 2013).
In the near future, we also plan to unify GLÀFF and WiktionaryX to give access to definitions and semantic relations in addition to inflectional and phonological information. Such a resource will be useful for NLP but also for
linguistic descriptions. More generally, multiple specific
lexicons may be derived from GLÀFF, depending on the
needs. For example, we illustrated in (Calderone et al.,
3
http://redac.univ-tlse2.fr/glaffoli/
2014) how we have built a psycholinguistics-oriented lexicon from GLÀFF by adding an extended set of features that
are used to set up experimental material in this field.
4.
References
Anton Pérez, L., Gonçalo Oliveira, H., and Gomes, P.
(2011). Extracting Lexical-Semantic Knowledge from
the Portuguese Wiktionary. In Proceedings of the 15th
Portuguese Conference on Artificial Intelligence, EPIA
2011, pages 703–717, Lisbon, Portugal.
Baroni, M., Bernardini, S., Ferraresi, A., and Zanchetta, E.
(2009). The WaCky wide web: a collection of very large
linguistically processed web-crawled corpora. Language
Resources and Evaluation, 43(3):209–226.
Boula De Mareuil, P., Yvon, F., D’Alessandro, C.,
Aubergé, V., Vaissière, J., and Amelot, A. (2000).
A French Phonetic Lexicon with variants for Speech
and Language Processing. In Proceedings of the Second International Conference on Language Resources
and Evaluation (LREC 2000), pages 273–276, Athens,
Greece.
Calderone, B., Hathout, N., and Sajous, F. (2014). From
GLÀFF to PsychoGLÀFF: a large psycholinguisticsoriented French lexical resource. In Proceedings of the
16th EURALEX International Congress, Bolzano, Italy.
Clément, L., Lang, B., and Sagot, B. (2004). Morphology based automatic acquisition of large-coverage lexica. In Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC
2004), pages 1841–1844, Lisbon, Portugal.
Detey, S., Durand, J., Laks, B., and Lyche, C. (2010). Les
variétés du français parlé dans l’espace francophone.
L’essentiel francais. Ophrys.
Gonçalo Oliveira, H. and Gomes, P. (2010). Onto.PT:
Automatic Construction of a Lexical Ontology for Portuguese. In Proceedings of 5th European Starting AI Researcher Symposium, pages 199–211, Lisbon, Portugal.
Gurevych, I., Eckle-Kohler, J., Hartmann, S., Matuschek,
M., Meyer, C. M., and Wirth, C. (2012). UBY - A
Large-Scale Unified Lexical-Semantic Resource Based
on LMF. In Proceedings of the 13th Conference of
1011
the European Chapter of the Association for Computational Linguistics (EACL 2012), pages 580–590, Avignon, France.
Martinet, A. and Walter, H. (1973). Dictionnaire de la
Prononciation Française dans son Usage Réel. France
Expansion.
Meyer, C. M. and Gurevych, I. (2012). OntoWiktionary
– Constructing an Ontology from the Collaborative Online Dictionary Wiktionary. In Pazienza, M. T. and
Stellato, A., editors, Semi-Automatic Ontology Development: Processes and Resources, chapter 6, pages 131–
161. IGI Global, Hershey, PA, USA.
Navarro, E., Sajous, F., Gaume, B., Prévot, L., Hsieh, S.,
Kuo, I., Magistry, P., and Huang, C.-R. (2009). Wiktionary and NLP: Improving synonymy networks. In
Proceedings of the 2009 ACL-IJCNLP Workshop on The
People’s Web Meets NLP: Collaboratively Constructed
Semantic Resources, pages 19–27, Singapore.
New, B. (2006). Lexique 3 : Une nouvelle base de données
lexicales. In Verbum ex machina. Actes de la 13e
conférence sur le Traitement Automatique des Langues
Naturelles (TALN’2006), Louvain-la-Neuve, Belgique.
Pérennou, G. and de Calmès, M. (1987). BDLEX lexical
data and knowledge base of spoken and written French.
In Proceedings of the European Conference on Speech
Technology, ECST 1987, pages 1393–1396, Edinburgh,
Scotland.
Rajman, M., Lecomte, J., and Paroubek, P. (1997). Format
de description lexicale pour le français. Partie 2 : Description morpho-syntaxique. Technical report, EPFL &
INaLF. GRACE GTR-3-2.1.
Romary, L., Salmon-Alt, S., and Francopoulo, G. (2004).
Standards going concrete: from LMF to Morphalou. In
Zock, M. and Saint-Dizier, P., editors, COLING 2004
Enhancing and using electronic dictionaries, pages 22–
28, Geneva, Switzerland.
Rychlý, P. (2007). Manatee/Bonito - A Modular Corpus
Manager. In Proceedings of the 1st Workshop on Recent Advances in Slavonic Natural Language Processing, pages 65–70, Brno, Czech Republic.
Sajous, F., Navarro, E., Gaume, B., Prévot, L., and Chudy,
Y. (2010). Semi-automatic Endogenous Enrichment of
Collaboratively Constructed Lexical Resources: Piggybacking onto Wiktionary. In Loftsson, H., Rögnvaldsson, E., and Helgadóttir, S., editors, Advances in Natural Language Processing, volume 6233 of LNCS, pages
332–344. Springer Berlin / Heidelberg.
Sajous, F., Hathout, N., and Calderone, B. (2013a).
GLÀFF, un Gros Lexique À tout Faire du Français. In
Actes de la 20e conférence sur le Traitement Automatique des Langues Naturelles (TALN’2013), pages 285–
298, Les Sables d’Olonne, France.
Sajous, F., Navarro, E., Gaume, B., Prévot, L., and Chudy,
Y. (2013b). Semi-automatic enrichment of crowdsourced synonymy networks: the WISIGOTH system
applied to Wiktionary. Language Resources and Evaluation, 47(1):63–96.
Schultz, T., Vu, N. T., and Schlippe, T. (2013). GlobalPhone: A multilingual text & speech database in 20
languages. In Proceedings of Conference on Acoustics,
Speech, and Signal Processing, pages 8126–8130, Vancouver, Canada.
Sérasset, G. (2012). Dbnary: Wiktionary as a LMF based
Multilingual RDF network. In Proceedings of the Eigth
International Conference on Language Resources and
Evaluation (LREC 2012), Istanbul, Turkey.
Urieli, A. (2013). Robust French syntax analysis: reconciling statistical methods and linguistic knowledge in the
Talismane toolkit. Ph.D. thesis, Université de ToulouseLe Mirail.
Zesch, T., Müller, C., and Gurevych, I. (2008). Extracting
Lexical Semantic Knowledge from Wikipedia and Wiktionary. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC
2008), Marrakech, Morocco.
1012